Adversarial machine learning

I just got back from a very good conference organized by startup.ml: Please read on for my to comments on part of one of the very good talks. Classic machine learning (especially as it is taught in classes) emphasizes a nice safe static environment where you are given some unchanging data and are asked to produce a nice predictive model one time. It is formally easier that casual inference or statistical inference as being right often is enough, no matter what the reason. Adversarial machine learning is the formal name for studying what happens when conceding even a slightly more realistic alternative to assumptions of these types (harmlessly called "relaxing assumptions").